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未验证 提交 c5f1cea6 编写于 作者: W WangLiu 提交者： GitHub
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Merge pull request #500 from codeWorm2015/develop 

fix #499 modify cmake add paddle mobile interface
上级 a4673c38 67554ae6
隐藏空白更改
内联 并排
Showing with 499 addition and 311 deletion
CMakeLists.txt
浏览文件 @ c5f1cea6
......@@ -40,8 +40,6 @@ else()
list(REMOVE_ITEM PADDLE_MOBILE_CC ${CMAKE_CURRENT_SOURCE_DIR}/src/operators/kernel/mali/*.h)
list(REMOVE_ITEM PADDLE_MOBILE_CC ${CMAKE_CURRENT_SOURCE_DIR}/src/operators/kernel/mali/*.cc)
list(REMOVE_ITEM PADDLE_MOBILE_CC ${CMAKE_CURRENT_SOURCE_DIR}/src/operators/kernel/mali/*.cpp)
endif()
if(FPGA)
......@@ -103,6 +101,10 @@ set(CMAKE_ARCHIVE_OUTPUT_DIRECTORY build)
set(CMAKE_LIBRARY_OUTPUT_DIRECTORY build)
set(CMAKE_RUNTIME_OUTPUT_DIRECTORY build)
# NET default
set(NET "defult" CACHE STRING "select net type")
set_property(CACHE NET PROPERTY STRINGS "defult" "googlenet" "mobilenet" "yolo" "squeezenet")
include("${CMAKE_CURRENT_LIST_DIR}/tools/op.cmake")
# if (IS_IOS)
......@@ -118,4 +120,3 @@ if(DEBUGING)
add_subdirectory(test)
endif()
src/io/io.cpp → src/io/executor.cpp
浏览文件 @ c5f1cea6
......@@ -12,7 +12,7 @@ WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "io/io.h"
#include "io/executor.h"
#include <algorithm>
#include <vector>
#include "common/enforce.h"
......@@ -39,7 +39,7 @@ char *Get_binary_data(std::string filename) {
PADDLE_MOBILE_ENFORCE(file != nullptr, "can't open file: %s ",
filename.c_str());
fseek(file, 0, SEEK_END);
long size = ftell(file);
int64_t size = ftell(file);
PADDLE_MOBILE_ENFORCE(size > 0, "size is too small");
rewind(file);
char *data = new char[size];
......@@ -50,116 +50,6 @@ char *Get_binary_data(std::string filename) {
return data;
}
static size_t ReadBuffer(const char *file_name, uint8_t **out) {
printf("%s \n", file_name);
FILE *fp;
fp = fopen(file_name, "rb");
PADDLE_MOBILE_ENFORCE(fp != NULL, " %s open failed !", file_name);
fseek(fp, 0, SEEK_END);
size_t size = ftell(fp);
rewind(fp);
DLOG << "model size: " << size;
*out = reinterpret_cast<uint8_t *>(malloc(size));
size_t cur_len = 0;
size_t nread;
while ((nread = fread(*out + cur_len, 1, size - cur_len, fp)) != 0) {
cur_len += nread;
}
fclose(fp);
return cur_len;
}
template <typename Dtype, Precision P>
const framework::Program<Dtype, P> Loader<Dtype, P>::Load(
const std::string &dirname, bool optimize, bool can_add_split) {
auto program =
this->LoadProgram(dirname + "/__model__", optimize, can_add_split);
program.model_path = dirname;
return program;
}
template <typename Dtype, Precision P>
const framework::Program<Dtype, P> Loader<Dtype, P>::Load(
const std::string &model_path, const std::string &para_path,
bool optimize) {
auto program = this->LoadProgram(model_path, optimize);
program.para_path = para_path;
program.combined = true;
return program;
}
template <typename Dtype, Precision P>
const framework::Program<Dtype, P> Loader<Dtype, P>::LoadProgram(
const std::string &model_path, bool optimize, bool can_add_split) {
std::string model_filename = model_path;
PaddleMobile__Framework__Proto__ProgramDesc *c_program;
uint8_t *buf = NULL;
size_t read_size = ReadBuffer(model_filename.c_str(), &buf);
PADDLE_MOBILE_ENFORCE(buf != NULL, "read from __model__ is null");
c_program = paddle_mobile__framework__proto__program_desc__unpack(
NULL, read_size, buf);
//
PADDLE_MOBILE_ENFORCE(c_program != NULL, "program is null");
//
DLOG << "n_ops: " << (*c_program->blocks)->n_ops;
//
auto originProgramDesc = std::make_shared<framework::ProgramDesc>(c_program);
framework::Program<Dtype, P> program;
program.originProgram = originProgramDesc;
auto scope = std::make_shared<framework::Scope>();
program.scope = scope;
for (const auto &block : originProgramDesc->Blocks()) {
for (auto var_desc : block->Vars()) {
auto var = scope->Var(var_desc->Name());
if (var_desc->Type() == framework::VARTYPE_TYPE_LOD_TENSOR) {
if (var_desc->Persistable() &&
var_desc->Type() != framework::VARTYPE_TYPE_FEED_MINIBATCH &&
var_desc->Type() != framework::VARTYPE_TYPE_FETCH_LIST) {
auto dim = var_desc->Tensor_desc().Dims();
auto tensor = var->GetMutable<framework::LoDTensor>();
tensor->Resize(framework::make_ddim(dim));
} else {
auto dim = var_desc->Tensor_desc().Dims();
PADDLE_MOBILE_ENFORCE(dim.size() > 0, "dim size is 0");
dim[0] = 1;
auto tensor = var->GetMutable<framework::LoDTensor>();
tensor->Resize(framework::make_ddim(dim));
}
} else {
// TODO(codeWorm): some.
}
}
}
if (optimize) {
framework::ProgramOptimize program_optimize;
program.optimizeProgram =
program_optimize.FusionOptimize(originProgramDesc, can_add_split);
}
if (optimize) {
program.optimizeProgram->Description("optimize: ");
} else {
originProgramDesc->Description("program: ");
}
paddle_mobile__framework__proto__program_desc__free_unpacked(c_program, NULL);
return program;
}
template class Loader<CPU, Precision::FP32>;
template class Loader<FPGA, Precision::FP32>;
template class Loader<GPU_MALI, Precision::FP32>;
#pragma mark - executor
template <typename Dtype, Precision P>
Executor<Dtype, P>::Executor(const framework::Program<Dtype> p, int batch_size,
......@@ -209,30 +99,30 @@ Executor<Dtype, P>::Executor(const framework::Program<Dtype> p, int batch_size,
template <typename Dtype, Precision P>
void Executor<Dtype, P>::LoadMemory(const framework::VarDesc var_desc,
framework::LoDTensor *tensor, char *&data) {
framework::LoDTensor *tensor, char **data) {
// 1. version
uint32_t version = *(uint32_t *)data;
data += sizeof(uint32_t);
uint32_t version = *reinterpret_cast<uint32_t *>(*data);
(*data) += sizeof(uint32_t);
// 2 Lod information
uint64_t *lod_level_ptr = new uint64_t();
memcpy(lod_level_ptr, data, sizeof(uint64_t));
memcpy(lod_level_ptr, (*data), sizeof(uint64_t));
uint64_t lod_level = *lod_level_ptr;
delete lod_level_ptr;
data += sizeof(uint64_t);
(*data) += sizeof(uint64_t);
auto &lod = *tensor->mutable_lod();
lod.resize(lod_level);
for (uint64_t i = 0; i < lod_level; ++i) {
uint64_t size = *(uint64_t *)data;
data += sizeof(uint64_t);
uint64_t size = *reinterpret_cast<uint64_t *>(*data);
(*data) += sizeof(uint64_t);
DLOG << "lod size: " << i << size;
std::vector<size_t> tmp(size / sizeof(size_t));
for (int k = 0; k < tmp.size(); ++k) {
tmp[k] = *(size_t *)data;
DLOG << "tmp[k]: " << k << *(size_t *)data;
data += sizeof(size_t);
tmp[k] = *reinterpret_cast<size_t *>(*data);
(*data) += sizeof(size_t);
}
for (auto j : tmp) {
......@@ -242,18 +132,18 @@ void Executor<Dtype, P>::LoadMemory(const framework::VarDesc var_desc,
}
// 3. tensor version
uint32_t tensor_version = *(uint32_t *)data;
data += sizeof(uint32_t);
uint32_t tensor_version = *reinterpret_cast<uint32_t *>(*data);
(*data) += sizeof(uint32_t);
// 4. tensor desc
int32_t size = *(int32_t *)data;
data += sizeof(int32_t);
int32_t size = *reinterpret_cast<int32_t *>(*data);
(*data) += sizeof(int32_t);
std::unique_ptr<char[]> buf(new char[size]);
for (int m = 0; m < size; ++m) {
buf.get()[m] = data[m];
buf.get()[m] = (*data)[m];
}
data += (sizeof(char) * size);
(*data) += (sizeof(char) * size);
const framework::TensorDesc &desc = var_desc.Tensor_desc();
int memory_size = 1;
......@@ -290,9 +180,9 @@ void Executor<Dtype, P>::LoadMemory(const framework::VarDesc var_desc,
}
for (int n = 0; n < memory_size * type_size; ++n) {
static_cast<char *>(memory)[n] = data[n];
static_cast<char *>(memory)[n] = (*data)[n];
}
data += (sizeof(char) * memory_size * type_size);
(*data) += (sizeof(char) * memory_size * type_size);
}
template <typename Dtype, Precision P>
......@@ -309,7 +199,7 @@ void Executor<Dtype, P>::InitMemory() {
char *origin_data =
Get_binary_data(program_.model_path + "/" + var_desc->Name());
char *data = origin_data;
LoadMemory(*var_desc, tensor, data);
LoadMemory(*var_desc, tensor, &data);
delete origin_data;
} else {
if (var_desc->Type() == framework::VARTYPE_TYPE_LOD_TENSOR) {
......@@ -335,7 +225,7 @@ void Executor<Dtype, P>::InitCombineMemory() {
if (var_desc->Name() == "feed" || var_desc->Name() == "fetch") {
continue;
}
LoadMemory(*var_desc, tensor, data);
LoadMemory(*var_desc, tensor, &data);
} else {
if (var_desc->Type() == framework::VARTYPE_TYPE_LOD_TENSOR) {
auto tensor = var->template GetMutable<framework::LoDTensor>();
......@@ -442,7 +332,8 @@ std::shared_ptr<framework::Tensor> Executor<Dtype, P>::Predict(
*(program_.scope));
#ifdef PADDLE_MOBILE_PROFILE
#ifdef PADDLE_EXECUTOR_MULTITHREAD
// TODO expose profile info as an interface, user can get them to analysis
// TODO(haipeng): expose profile info as an interface, user can get them to
// analysis
// the performance of their deepnet.
FILE *df = fopen("net.dot", "w");
fprintf(df, "digraph {\n");
......@@ -480,8 +371,9 @@ std::shared_ptr<framework::Tensor> Executor<Dtype, P>::Predict(
std::sort(_tv.begin(), _tv.end(), compf);
_tv.push_back(std::make_pair("total", _ptotal));
for (auto const &p : _tv) {
printf("%-16s\t%-10.0f\t%-2.4f\n", p.first.c_str(), (float)p.second,
(float)p.second / _ptotal * 100.0);
printf("%-16s\t%-10.0f\t%-2.4f\n", p.first.c_str(),
static_cast<float>(p.second),
static_cast<float>(p.second) / _ptotal * 100.0);
}
printf("====================[---------]======================\n");
#endif
......
src/io/io.h → src/io/executor.h
浏览文件 @ c5f1cea6
......@@ -18,6 +18,7 @@ limitations under the License. */
#include <memory>
#include <string>
#include <vector>
#include "common/types.h"
#include "framework/lod_tensor.h"
#include "framework/operator.h"
......@@ -32,31 +33,6 @@ limitations under the License. */
namespace paddle_mobile {
template <typename Dtype = CPU, Precision P = Precision::FP32>
class Loader {
public:
/*
* @b load separate format fluid model
* @b 加载分开形式的 fluid 模型
* */
const framework::Program<Dtype, P> Load(const std::string &dirname,
bool optimize = false,
bool can_add_split = false);
/*
* @b load combine format fluid mode
* @b 加载结合在一起格式的模型
* */
const framework::Program<Dtype, P> Load(const std::string &model_path,
const std::string &para_path,
bool optimize = false);
private:
const framework::Program<Dtype, P> LoadProgram(const std::string &model_path,
bool optimize = false,
bool can_add_split = false);
};
template <typename Dtype = CPU, Precision P = Precision::FP32>
class Executor {
public:
......@@ -86,7 +62,7 @@ class Executor {
Executor() = default;
void InitMemory();
void LoadMemory(const framework::VarDesc var_desc,
framework::LoDTensor *tensor, char *&data);
framework::LoDTensor *tensor, char **data);
void InitCombineMemory();
framework::Program<Dtype> program_;
int batch_size_ = 1;
......
src/io/loader.cpp 0 → 100644
浏览文件 @ c5f1cea6
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "io/loader.h"
#include "framework/lod_tensor.h"
#include "framework/program/program-optimize/program_optimize.h"
namespace paddle_mobile {
using framework::Variable;
static size_t ReadBuffer(const char *file_name, uint8_t **out) {
printf("%s \n", file_name);
FILE *fp;
fp = fopen(file_name, "rb");
PADDLE_MOBILE_ENFORCE(fp != NULL, " %s open failed !", file_name);
fseek(fp, 0, SEEK_END);
size_t size = ftell(fp);
rewind(fp);
DLOG << "model size: " << size;
*out = reinterpret_cast<uint8_t *>(malloc(size));
size_t cur_len = 0;
size_t nread;
while ((nread = fread(*out + cur_len, 1, size - cur_len, fp)) != 0) {
cur_len += nread;
}
fclose(fp);
return cur_len;
}
template <typename Dtype, Precision P>
const framework::Program<Dtype, P> Loader<Dtype, P>::Load(
const std::string &dirname, bool optimize, bool can_add_split) {
auto program =
this->LoadProgram(dirname + "/__model__", optimize, can_add_split);
program.model_path = dirname;
return program;
}
template <typename Dtype, Precision P>
const framework::Program<Dtype, P> Loader<Dtype, P>::Load(
const std::string &model_path, const std::string &para_path,
bool optimize) {
auto program = this->LoadProgram(model_path, optimize);
program.para_path = para_path;
program.combined = true;
return program;
}
template <typename Dtype, Precision P>
const framework::Program<Dtype, P> Loader<Dtype, P>::LoadProgram(
const std::string &model_path, bool optimize, bool can_add_split) {
std::string model_filename = model_path;
PaddleMobile__Framework__Proto__ProgramDesc *c_program;
uint8_t *buf = NULL;
size_t read_size = ReadBuffer(model_filename.c_str(), &buf);
PADDLE_MOBILE_ENFORCE(buf != NULL, "read from __model__ is null");
c_program = paddle_mobile__framework__proto__program_desc__unpack(
NULL, read_size, buf);
//
PADDLE_MOBILE_ENFORCE(c_program != NULL, "program is null");
//
DLOG << "n_ops: " << (*c_program->blocks)->n_ops;
//
auto originProgramDesc = std::make_shared<framework::ProgramDesc>(c_program);
framework::Program<Dtype, P> program;
program.originProgram = originProgramDesc;
auto scope = std::make_shared<framework::Scope>();
program.scope = scope;
for (const auto &block : originProgramDesc->Blocks()) {
for (auto var_desc : block->Vars()) {
auto var = scope->Var(var_desc->Name());
if (var_desc->Type() == framework::VARTYPE_TYPE_LOD_TENSOR) {
if (var_desc->Persistable() &&
var_desc->Type() != framework::VARTYPE_TYPE_FEED_MINIBATCH &&
var_desc->Type() != framework::VARTYPE_TYPE_FETCH_LIST) {
auto dim = var_desc->Tensor_desc().Dims();
auto tensor = var->GetMutable<framework::LoDTensor>();
tensor->Resize(framework::make_ddim(dim));
} else {
auto dim = var_desc->Tensor_desc().Dims();
PADDLE_MOBILE_ENFORCE(dim.size() > 0, "dim size is 0");
dim[0] = 1;
auto tensor = var->GetMutable<framework::LoDTensor>();
tensor->Resize(framework::make_ddim(dim));
}
} else {
// TODO(codeWorm): some.
}
}
}
if (optimize) {
framework::ProgramOptimize program_optimize;
program.optimizeProgram =
program_optimize.FusionOptimize(originProgramDesc, can_add_split);
}
if (optimize) {
program.optimizeProgram->Description("optimize: ");
} else {
originProgramDesc->Description("program: ");
}
paddle_mobile__framework__proto__program_desc__free_unpacked(c_program, NULL);
return program;
}
template class Loader<CPU, Precision::FP32>;
template class Loader<FPGA, Precision::FP32>;
template class Loader<GPU_MALI, Precision::FP32>;
} // namespace paddle_mobile
src/io/loader.h 0 → 100644
浏览文件 @ c5f1cea6
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <string>
#include "common/types.h"
#include "framework/program/program.h"
namespace paddle_mobile {
template <typename Dtype = CPU, Precision P = Precision::FP32>
class Loader {
public:
/*
* @b load separate format fluid model
* @b 加载分开形式的 fluid 模型
* */
const framework::Program<Dtype, P> Load(const std::string &dirname,
bool optimize = false,
bool can_add_split = false);
/*
* @b load combine format fluid mode
* @b 加载结合在一起格式的模型
* */
const framework::Program<Dtype, P> Load(const std::string &model_path,
const std::string &para_path,
bool optimize = false);
private:
const framework::Program<Dtype, P> LoadProgram(const std::string &model_path,
bool optimize = false,
bool can_add_split = false);
};
} // namespace paddle_mobile
src/io/paddle_mobile.cpp 0 → 100644
浏览文件 @ c5f1cea6
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
//
// Created by liuRuiLong on 2018/7/2.
//
#include "io/paddle_mobile.h"
namespace paddle_mobile {
template <typename Dtype, Precision P>
bool PaddleMobile<Dtype, P>::Load(const std::string &dirname, bool optimize,
int batch_size) {
if (loader_.get() == nullptr) {
loader_ = std::make_shared<Loader<Dtype, P>>();
} else {
LOG(kLOG_INFO) << "loader inited";
}
if (executor_.get() == nullptr) {
executor_ = std::make_shared<Executor<Dtype, P>>(
loader_->Load(dirname, optimize), batch_size, optimize);
} else {
LOG(kLOG_INFO) << "executor inited";
}
return true;
}
template <typename Dtype, Precision P>
bool PaddleMobile<Dtype, P>::Load(const std::string &model_path,
const std::string &para_path, bool optimize,
int batch_size) {
if (loader_.get() == nullptr) {
loader_ = std::make_shared<Loader<Dtype, P>>();
} else {
LOG(kLOG_INFO) << "loader inited";
}
if (executor_.get() == nullptr) {
executor_ = std::make_shared<Executor<Dtype, P>>(
loader_->Load(model_path, para_path, optimize), batch_size, optimize);
} else {
LOG(kLOG_INFO) << "executor inited";
}
return true;
}
template <typename Dtype, Precision P>
std::shared_ptr<framework::Tensor> PaddleMobile<Dtype, P>::Predict(
const framework::Tensor &t) {
return executor_->Predict(t);
}
template <typename Dtype, Precision P>
std::vector<typename PaddleMobile<Dtype, P>::Ptype>
PaddleMobile<Dtype, P>::Predict(const std::vector<Ptype> &input,
const std::vector<int64_t> &dims) {
return executor_->Predict(input, dims);
}
template <typename Dtype, Precision P>
void PaddleMobile<Dtype, P>::Clear() {
executor_ = nullptr;
loader_ = nullptr;
}
template class PaddleMobile<CPU, Precision::FP32>;
template class PaddleMobile<FPGA, Precision::FP32>;
template class PaddleMobile<GPU_MALI, Precision::FP32>;
} // namespace paddle_mobile
src/io/paddle_mobile.h 0 → 100644
浏览文件 @ c5f1cea6
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <memory>
#include <string>
#include <vector>
#include "common/types.h"
#include "framework/tensor.h"
#include "io/executor.h"
#include "io/loader.h"
namespace paddle_mobile {
template <typename Dtype = CPU, Precision P = Precision::FP32>
class PaddleMobile {
typedef typename PrecisionTrait<P>::ptype Ptype;
public:
PaddleMobile() {}
/*
* @b load separate format fluid model
* @b 加载分开形式的 fluid 模型
* */
bool Load(const std::string &dirname, bool optimize = false,
int batch_size = 1);
/*
* @b load combine format fluid mode
* @b 加载结合在一起格式的模型
* */
bool Load(const std::string &model_path, const std::string &para_path,
bool optimize = false, int batch_size = 1);
/*
* @b to predict
* */
std::shared_ptr<framework::Tensor> Predict(const framework::Tensor &t);
/*
* @b to predict with vector and dim
*
* @b 使用 输入 和 输入的维度信息 进行预测
* */
std::vector<Ptype> Predict(const std::vector<Ptype> &input,
const std::vector<int64_t> &dims);
void Clear();
private:
std::shared_ptr<Loader<Dtype, P>> loader_;
std::shared_ptr<Executor<Dtype, P>> executor_;
};
} // namespace paddle_mobile
src/jni/paddle_mobile_jni.cpp
浏览文件 @ c5f1cea6
......@@ -15,6 +15,10 @@ limitations under the License. */
#ifdef ANDROID
#include "paddle_mobile_jni.h"
#include "common/log.h"
#include "framework/tensor.h"
#include "io/paddle_mobile.h"
#ifdef __cplusplus
extern "C" {
#endif
......@@ -28,17 +32,16 @@ using std::string;
extern const char *ANDROID_LOG_TAG =
"paddle_mobile LOG built on " __DATE__ " " __TIME__;
static Executor<CPU> *shared_executor_instance = nullptr;
static PaddleMobile<CPU> *shared_paddle_mobile_instance = nullptr;
// toDo mutex lock
// static std::mutex shared_mutex;
Executor<CPU> *getExecutorInstance(const Program<CPU> p, int batch_size,
bool use_optimize) {
if (nullptr == shared_executor_instance) {
shared_executor_instance = new Executor<CPU>(p, batch_size, use_optimize);
PaddleMobile<CPU> *getPaddleMobileInstance() {
if (nullptr == shared_paddle_mobile_instance) {
shared_paddle_mobile_instance = new PaddleMobile<CPU>();
}
return shared_executor_instance;
return shared_paddle_mobile_instance;
}
string jstring2cppstring(JNIEnv *env, jstring jstr) {
......@@ -51,11 +54,9 @@ string jstring2cppstring(JNIEnv *env, jstring jstr) {
JNIEXPORT jboolean JNICALL Java_com_baidu_paddle_PML_load(JNIEnv *env,
jclass thiz,
jstring modelPath) {
paddle_mobile::Loader<paddle_mobile::CPU> loader;
bool optimize = true;
auto program = loader.Load(jstring2cppstring(env, modelPath), optimize);
shared_executor_instance = getExecutorInstance(program, 1, optimize);
return shared_executor_instance != nullptr ? JNI_TRUE : JNI_FALSE;
return getPaddleMobileInstance()->Load(jstring2cppstring(env, modelPath),
optimize);
}
JNIEXPORT jfloatArray JNICALL Java_com_baidu_paddle_PML_predictImage(
......@@ -73,7 +74,7 @@ JNIEXPORT jfloatArray JNICALL Java_com_baidu_paddle_PML_predictImage(
for (int i = 0; i < framework::product(ddim); i++) {
input_ptr[i] = dataPointer[i];
}
auto output = shared_executor_instance->Predict(input);
auto output = shared_paddle_mobile_instance->Predict(input);
count = output->numel();
result = env->NewFloatArray(count);
env->SetFloatArrayRegion(result, 0, count, output->data<float>());
......@@ -81,7 +82,9 @@ JNIEXPORT jfloatArray JNICALL Java_com_baidu_paddle_PML_predictImage(
}
JNIEXPORT void JNICALL Java_com_baidu_paddle_PML_clear(JNIEnv *env,
jclass thiz) {}
jclass thiz) {
getPaddleMobileInstance()->Clear();
}
} // namespace jni
} // namespace paddle_mobile
......
src/jni/paddle_mobile_jni.h
浏览文件 @ c5f1cea6
......@@ -15,9 +15,6 @@ limitations under the License. */
#pragma once
#ifdef ANDROID
#include <jni.h>
#include "common/log.h"
#include "framework/tensor.h"
#include "io/io.h"
#ifdef __cplusplus
extern "C" {
......
test/CMakeLists.txt
浏览文件 @ c5f1cea6
set(dir ${CMAKE_CURRENT_SOURCE_DIR})
set(CMAKE_RUNTIME_OUTPUT_DIRECTORY "${dir}/build")
if (googlenet)
if (NET STREQUAL "googlenet")
# gen test
ADD_EXECUTABLE(test-googlenet net/test_googlenet.cpp test_helper.h test_include.h executor_for_test.h)
target_link_libraries(test-googlenet paddle-mobile)
elseif (mobilenet)
elseif (NET STREQUAL "mobilenet")
# gen test
ADD_EXECUTABLE(test-mobilenet net/test_mobilenet.cpp test_helper.h test_include.h executor_for_test.h)
target_link_libraries(test-mobilenet paddle-mobile)
elseif (yolo)
elseif (NET STREQUAL "yolo")
# gen test
ADD_EXECUTABLE(test-yolo net/test_yolo.cpp test_helper.h test_include.h executor_for_test.h)
target_link_libraries(test-yolo paddle-mobile)
elseif (squeezenet)
elseif (NET STREQUAL "squeezenet")
# gen test
ADD_EXECUTABLE(test-squeezenet net/test_squeezenet.cpp test_helper.h test_include.h executor_for_test.h)
target_link_libraries(test-squeezenet paddle-mobile)
elseif(resnet)
elseif(NET STREQUAL "resnet")
# gen test
ADD_EXECUTABLE(test-resnet net/test_resnet.cpp test_helper.h test_include.h executor_for_test.h)
target_link_libraries(test-resnet paddle-mobile)
......
test/executor_for_test.h
浏览文件 @ c5f1cea6
......@@ -19,7 +19,7 @@ limitations under the License. */
#include "common/log.h"
#include "framework/op_registry.h"
#include "io/io.h"
#include "io/executor.h"
#include "operators/conv_op.h"
#include "operators/elementwise_add_op.h"
#include "operators/pool_op.h"
......
test/framework/test_load.cpp
浏览文件 @ c5f1cea6
......@@ -13,7 +13,7 @@ See the License for the specific language governing permissions and
limitations under the License. */
#include "../test_helper.h"
#include "io/io.h"
#include "io/loader.h"
int main() {
paddle_mobile::Loader<paddle_mobile::CPU> loader;
......
test/framework/test_optimize.cpp
浏览文件 @ c5f1cea6
......@@ -15,7 +15,7 @@ limitations under the License. */
#include "../test_helper.h"
#include "framework/program/program-optimize/node.h"
#include "framework/program/program-optimize/program_optimize.h"
#include "io/io.h"
#include "io/loader.h"
int main() {
paddle_mobile::Loader<paddle_mobile::CPU> loader;
......
test/net/test_googlenet.cpp
浏览文件 @ c5f1cea6
......@@ -17,25 +17,25 @@ limitations under the License. */
#include "../test_include.h"
int main() {
paddle_mobile::Loader<paddle_mobile::CPU> loader;
paddle_mobile::PaddleMobile<paddle_mobile::CPU> paddle_mobile;
bool optimize = true;
auto time1 = time();
// auto program = loader.Load(g_googlenet, optimize);
auto program = loader.Load(g_googlenet_combine + "/model",
g_googlenet_combine + "/params", optimize);
auto time2 = time();
DLOG << "load cost :" << time_diff(time1, time2) << "ms\n";
paddle_mobile::Executor<paddle_mobile::CPU> executor(program, 1, optimize);
std::vector<float> input;
std::vector<int64_t> dims{1, 3, 224, 224};
GetInput<float>(g_test_image_1x3x224x224, &input, dims);
auto time3 = time();
if (paddle_mobile.Load(g_googlenet_combine + "/model",
g_googlenet_combine + "/params", optimize)) {
auto time2 = time();
DLOG << "load cost :" << time_diff(time1, time2) << "ms\n";
std::vector<float> input;
std::vector<int64_t> dims{1, 3, 224, 224};
GetInput<float>(g_test_image_1x3x224x224, &input, dims);
auto time3 = time();
for (int i = 0; i < 10; ++i) {
executor.Predict(input, dims);
}
for (int i = 0; i < 10; ++i) {
paddle_mobile.Predict(input, dims);
}
auto time4 = time();
DLOG << "predict cost :" << time_diff(time3, time4) << "ms\n";
auto time4 = time();
DLOG << "predict cost :" << time_diff(time3, time4) << "ms\n";
}
return 0;
}
test/net/test_mobilenet+ssd.cpp
浏览文件 @ c5f1cea6
......@@ -17,23 +17,23 @@ limitations under the License. */
#include "../test_include.h"
int main() {
paddle_mobile::Loader<paddle_mobile::CPU> loader;
paddle_mobile::PaddleMobile<paddle_mobile::CPU> paddle_mobile;
auto time1 = time();
auto program = loader.Load(g_mobilenet_ssd, true);
auto time2 = time();
DLOG << "load cost :" << time_diff(time1, time1) << "ms";
paddle_mobile::Executor<paddle_mobile::CPU> executor(program, 1, true);
if (paddle_mobile.Load(g_mobilenet_ssd, true)) {
auto time2 = time();
DLOG << "load cost :" << time_diff(time1, time1) << "ms";
std::vector<int64_t> dims{1, 3, 300, 300};
Tensor input_tensor;
SetupTensor<float>(&input_tensor, {1, 3, 300, 300}, static_cast<float>(0),
static_cast<float>(1));
std::vector<int64_t> dims{1, 3, 300, 300};
Tensor input_tensor;
SetupTensor<float>(&input_tensor, {1, 3, 300, 300}, static_cast<float>(0),
static_cast<float>(1));
std::vector<float> input(input_tensor.data<float>(),
input_tensor.data<float>() + input_tensor.numel());
auto time3 = time();
executor.Predict(input, dims);
auto time4 = time();
DLOG << "predict cost :" << time_diff(time3, time4) << "ms";
std::vector<float> input(input_tensor.data<float>(),
input_tensor.data<float>() + input_tensor.numel());
auto time3 = time();
paddle_mobile.Predict(input, dims);
auto time4 = time();
DLOG << "predict cost :" << time_diff(time3, time4) << "ms";
}
return 0;
}
test/net/test_mobilenet.cpp
浏览文件 @ c5f1cea6
......@@ -17,24 +17,25 @@ limitations under the License. */
#include "../test_include.h"
int main() {
paddle_mobile::Loader<paddle_mobile::CPU> loader;
paddle_mobile::PaddleMobile<paddle_mobile::CPU> paddle_mobile;
auto time1 = time();
auto program = loader.Load(g_mobilenet, true);
auto time2 = time();
DLOG << "load cost :" << time_diff(time1, time1) << "ms";
paddle_mobile::Executor<paddle_mobile::CPU> executor(program, 1, true);
std::vector<int64_t> dims{1, 3, 224, 224};
Tensor input_tensor;
SetupTensor<float>(&input_tensor, {1, 3, 224, 224}, static_cast<float>(0),
static_cast<float>(1));
std::vector<float> input(input_tensor.data<float>(),
input_tensor.data<float>() + input_tensor.numel());
auto time3 = time();
auto vec_result = executor.Predict(input, dims);
auto time4 = time();
DLOG << "predict cost :" << time_diff(time3, time4) << "ms";
if (paddle_mobile.Load(g_mobilenet, true)) {
auto time2 = time();
DLOG << "load cost :" << time_diff(time1, time1) << "ms";
std::vector<int64_t> dims{1, 3, 224, 224};
Tensor input_tensor;
SetupTensor<float>(&input_tensor, {1, 3, 224, 224}, static_cast<float>(0),
static_cast<float>(1));
std::vector<float> input(input_tensor.data<float>(),
input_tensor.data<float>() + input_tensor.numel());
auto time3 = time();
auto vec_result = paddle_mobile.Predict(input, dims);
auto time4 = time();
DLOG << "predict cost :" << time_diff(time3, time4) << "ms";
}
return 0;
}
test/net/test_resnet.cpp
浏览文件 @ c5f1cea6
......@@ -17,23 +17,23 @@ limitations under the License. */
#include "../test_include.h"
int main() {
paddle_mobile::Loader<paddle_mobile::CPU> loader;
paddle_mobile::PaddleMobile<paddle_mobile::CPU> paddle_mobile;
auto time1 = time();
auto program = loader.Load(g_resnet, false);
auto time2 = time();
DLOG << "load cost :" << time_diff(time1, time1) << "ms";
paddle_mobile::Executor<paddle_mobile::CPU> executor(program, 1, false);
if (paddle_mobile.Load(g_resnet, false)) {
auto time2 = time();
DLOG << "load cost :" << time_diff(time1, time1) << "ms";
std::vector<int64_t> dims{1, 3, 32, 32};
Tensor input_tensor;
SetupTensor<float>(&input_tensor, {1, 3, 32, 32}, static_cast<float>(0),
static_cast<float>(1));
std::vector<int64_t> dims{1, 3, 32, 32};
Tensor input_tensor;
SetupTensor<float>(&input_tensor, {1, 3, 32, 32}, static_cast<float>(0),
static_cast<float>(1));
std::vector<float> input(input_tensor.data<float>(),
input_tensor.data<float>() + input_tensor.numel());
auto time3 = time();
paddle_mobile.Predict(input, dims);
auto time4 = time();
DLOG << "predict cost :" << time_diff(time3, time4) << "ms";
}
std::vector<float> input(input_tensor.data<float>(),
input_tensor.data<float>() + input_tensor.numel());
auto time3 = time();
executor.Predict(input, dims);
auto time4 = time();
DLOG << "predict cost :" << time_diff(time3, time4) << "ms";
return 0;
}
test/net/test_squeezenet.cpp
浏览文件 @ c5f1cea6
......@@ -17,25 +17,25 @@ limitations under the License. */
#include "../test_include.h"
int main() {
paddle_mobile::Loader<paddle_mobile::CPU> loader;
paddle_mobile::PaddleMobile<paddle_mobile::CPU> paddle_mobile;
// ../../../test/models/googlenet
// ../../../test/models/mobilenet
auto time1 = time();
auto program = loader.Load(g_squeezenet, false);
auto time2 = time();
DLOG << "load cost :" << time_diff(time1, time1) << "ms";
paddle_mobile::Executor<paddle_mobile::CPU> executor(program, 1, false);
if (paddle_mobile.Load(g_squeezenet, false)) {
auto time2 = time();
DLOG << "load cost :" << time_diff(time1, time1) << "ms";
std::vector<int64_t> dims{1, 3, 227, 227};
Tensor input_tensor;
SetupTensor<float>(&input_tensor, {1, 3, 227, 227}, static_cast<float>(0),
static_cast<float>(1));
std::vector<int64_t> dims{1, 3, 227, 227};
Tensor input_tensor;
SetupTensor<float>(&input_tensor, {1, 3, 227, 227}, static_cast<float>(0),
static_cast<float>(1));
std::vector<float> input(input_tensor.data<float>(),
input_tensor.data<float>() + input_tensor.numel());
auto time3 = time();
paddle_mobile.Predict(input, dims);
auto time4 = time();
DLOG << "predict cost :" << time_diff(time3, time4) << "ms";
}
std::vector<float> input(input_tensor.data<float>(),
input_tensor.data<float>() + input_tensor.numel());
auto time3 = time();
executor.Predict(input, dims);
auto time4 = time();
DLOG << "predict cost :" << time_diff(time3, time4) << "ms";
return 0;
}
test/net/test_yolo.cpp
浏览文件 @ c5f1cea6
......@@ -17,25 +17,25 @@ limitations under the License. */
#include "../test_include.h"
int main() {
paddle_mobile::Loader<paddle_mobile::CPU> loader;
paddle_mobile::PaddleMobile<paddle_mobile::CPU> paddle_mobile;
// ../../../test/models/googlenet
// ../../../test/models/mobilenet
auto time1 = time();
auto program = loader.Load(g_yolo, false);
auto time2 = time();
DLOG << "load cost :" << time_diff(time1, time1) << "ms";
paddle_mobile::Executor<paddle_mobile::CPU> executor(program, 1, false);
if (paddle_mobile.Load(g_yolo, false)) {
auto time2 = time();
DLOG << "load cost :" << time_diff(time1, time1) << "ms";
std::vector<int64_t> dims{1, 3, 227, 227};
Tensor input_tensor;
SetupTensor<float>(&input_tensor, {1, 3, 227, 227}, static_cast<float>(0),
static_cast<float>(1));
std::vector<int64_t> dims{1, 3, 227, 227};
Tensor input_tensor;
SetupTensor<float>(&input_tensor, {1, 3, 227, 227}, static_cast<float>(0),
static_cast<float>(1));
std::vector<float> input(input_tensor.data<float>(),
input_tensor.data<float>() + input_tensor.numel());
auto time3 = time();
executor.Predict(input, dims);
auto time4 = time();
DLOG << "predict cost :" << time_diff(time3, time4) << "ms";
std::vector<float> input(input_tensor.data<float>(),
input_tensor.data<float>() + input_tensor.numel());
auto time3 = time();
paddle_mobile.Predict(input, dims);
auto time4 = time();
DLOG << "predict cost :" << time_diff(time3, time4) << "ms";
}
return 0;
}
test/operators/test_sigmoid_op.cpp
浏览文件 @ c5f1cea6
......@@ -14,7 +14,7 @@ limitations under the License. */
#include "../../src/operators/kernel/sigmoid_kernel.h"
#include "../test_helper.h"
#include "io/io.h"
#include "io/executor.h"
int main() {
paddle_mobile::framework::Tensor input;
......
test/test_include.h
浏览文件 @ c5f1cea6
......@@ -30,4 +30,4 @@ limitations under the License. */
#include "framework/scope.h"
#include "framework/tensor.h"
#include "framework/variable.h"
#include "io/io.h"
#include "io/paddle_mobile.h"
tools/build.sh
浏览文件 @ c5f1cea6
......@@ -60,7 +60,6 @@ build_for_android() {
TOOLCHAIN_FILE="./tools/android-cmake/android.toolchain.cmake"
ANDROID_ARM_MODE="arm"
if [ $# -eq 1 ]; then
NET=$1
cmake .. \
-B"../build/release/${PLATFORM}" \
-DANDROID_ABI="${ABI}" \
......@@ -70,7 +69,7 @@ build_for_android() {
-DCMAKE_CXX_FLAGS="${CXX_FLAGS}" \
-DANDROID_STL=c++_static \
-DANDROID=true \
-D"${NET}=true" \
-DNET=$1 \
-D"${ARM_PLATFORM}"=true
else
......@@ -99,7 +98,6 @@ build_for_ios() {
CXX_FLAGS="-fobjc-abi-version=2 -fobjc-arc -std=gnu++14 -stdlib=libc++ -isysroot ${CMAKE_OSX_SYSROOT}"
mkdir -p "${BUILD_DIR}"
if [ $# -eq 1 ]; then
NET=$1
cmake .. \
-B"${BUILD_DIR}" \
-DCMAKE_BUILD_TYPE="${MODE}" \
......@@ -107,7 +105,7 @@ build_for_ios() {
-DIOS_PLATFORM=OS \
-DCMAKE_C_FLAGS="${C_FLAGS}" \
-DCMAKE_CXX_FLAGS="${CXX_FLAGS}" \
-D"${NET}"=true \
-DNET=$1 \
-DIS_IOS="true"
else
cmake .. \
......@@ -129,16 +127,12 @@ build_error() {
if [ $# -lt 1 ]; then
echo "error: target missing!"
echo "available targets: mac|linux|ios|android"
echo "sample usage: ./build.sh mac"
echo "available targets: ios|android"
echo "sample usage: ./build.sh android"
else
if [ $# -eq 2 ]; then
if [ $2 != "googlenet" -a $2 != "mobilenet" -a $2 != "yolo" -a $2 != "squeezenet" -a $2 != "resnet" ]; then
if [ $1 = "mac" ]; then
build_for_mac
elif [ $1 = "linux" ]; then
build_for_linux
elif [ $1 = "android" ]; then
if [ $1 = "android" ]; then
build_for_android
elif [ $1 = "ios" ]; then
build_for_ios
......@@ -146,11 +140,7 @@ else
build_error
fi
else
if [ $1 = "mac" ]; then
build_for_mac $2
elif [ $1 = "linux" ]; then
build_for_linux $2
elif [ $1 = "android" ]; then
if [ $1 = "android" ]; then
build_for_android $2
elif [ $1 = "ios" ]; then
build_for_ios $2
......@@ -159,11 +149,7 @@ else
fi
fi
else
if [ $1 = "mac" ]; then
build_for_mac
elif [ $1 = "linux" ]; then
build_for_linux
elif [ $1 = "android" ]; then
if [ $1 = "android" ]; then
build_for_android
elif [ $1 = "ios" ]; then
build_for_ios
......
tools/op.cmake
浏览文件 @ c5f1cea6
set(NET "googlenet" CACHE STRING "select net type")
set_property(CACHE NET PROPERTY STRINGS "defult" "googlenet" "mobilenet" "yolo" "squeezenet")
if (NET EQUAL "googlenet")
if (NET STREQUAL "googlenet")
set(CONCAT_OP ON)
set(CONV_OP ON)
set(LRN_OP ON)
......@@ -12,7 +9,7 @@ if (NET EQUAL "googlenet")
set(RELU_OP ON)
set(FUSION_CONVADD_OP ON)
set(FUSION_CONVADD_RELU_OP ON)
elseif (NET EQUAL "mobilenet")
elseif (NET STREQUAL "mobilenet")
set(CONV_OP ON)
set(ELEMENTWISEADD_OP ON)
set(RELU_OP ON)
......@@ -23,12 +20,12 @@ elseif (NET EQUAL "mobilenet")
set(POOL_OP ON)
set(RESHAPE_OP ON)
set(FUSION_CONVADDBNRELU_OP)
elseif (NET EQUAL "yolo")
elseif (NET STREQUAL "yolo")
set(BATCHNORM_OP ON)
set(CONV_OP ON)
set(RELU_OP ON)
set(ELEMENTWISEADD_OP ON)
elseif (NET EQUAL "squeezenet")
elseif (NET STREQUAL "squeezenet")
set(CONCAT_OP ON)
set(CONV_OP ON)
set(RELU_OP ON)
......@@ -36,7 +33,7 @@ elseif (NET EQUAL "squeezenet")
set(POOL_OP ON)
set(RESHAPE_OP ON)
set(SOFTMAX_OP ON)
elseif (NET EQUAL "resnet")
elseif (NET STREQUAL "resnet")
set(CONV_OP ON)
set(BATCHNORM_OP ON)
set(ELEMENTWISEADD_OP ON)
......
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